Purpose: To investigate the convergent validity, reliability, and sensitivity over a week of training of a standardized running test to measure neuromuscular fatigue. Methods: Twenty male rugby union players were recruited for the study, which took place during preseason. The standardized running test consisted of four 60-m runs paced at  ~5 m·s−1 with 33 seconds of recovery between trials. Data from micromechanical electrical systems were used to calculate a running-load index (RLI), which was a ratio between the mechanical load and the speed performed during runs. RLI was calculated by using either the entire duration of the run or a constant-velocity period. For each type of calculation, either an individual directional or the sum of the 3 components of the accelerometer was used. A measure of leg stiffness was used to assess the convergent validity of the RLI. Results: Unclear to large relationships between leg stiffness and RLI were found (r ranged from −.20 to .62). Regarding reliability, small to moderate (.47–.86) standardized typical errors were found. The sensitivity analysis showed that the leg stiffness presented a very likely trivial change over the course of 1 week of training, whereas RLI showed very likely small to a most likely large change. Conclusions: This study showed that RLI is a practical method to measure neuromuscular fatigue. In addition, such a methodology aligns with the constraint of elite team-sport setup due to its ease of implementation in practice.

Background: Student-athletes are subject to significant demands due to their concurrent sporting and academic commitments, which may affect their sleep. This study aimed to compare the self-reported sleep quality, quantity, and intraindividual variability (IIV) of students and student-athletes through an online survey. Hypothesis: Student-athletes will have a poorer sleep quality and quantity and experience more IIV. Study Design: Case-control study. Level of Evidence: Level 4. Methods: Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while sleep quantity and IIV were assessed using the Consensus Sleep Diary. Initially, the PSQI and additional questions regarding sport participation habits were completed by 138 participants (65 students, 73 student-athletes). From within this sample, 44 participants were recruited to complete the sleep diary for a period of 14 days. Results: The mean PSQI score was 6.89 ± 3.03, with 65% of the sample identified as poor sleepers, but no difference was observed between students and student-athletes. Analysis of sleep patterns showed only possibly to likely small differences in sleep schedule, sleep onset latency, and subjective sleep quality between groups. IIV analysis showed likely moderate to possibly small differences between groups, suggesting more variable sleep patterns among student-athletes. Conclusion: This study highlights that sleep issues are prevalent within the university student population and that student-athletes may be at greater risk due to more variable sleep patterns. Clinical Relevance: University coaches should consider these results to optimize sleep habits of their student-athletes.

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Background: Growing evidence highlights that elite rugby union players experience poor sleep quality and quantity which can be detrimental for performance. Objectives: This study aimed to i) compare objective sleep measures of rugby union players between age categories over a one week period, and ii) compare self-reported measures of sleep to wristwatch actigraphy as the criterion. Methods: Two hundred and fifty-three nights of sleep were recorded from 38 players representing four different age groups (i.e. under 16, under 18, senior academy, elite senior) in a professional rugby union club in the United Kingdom (UK). Linear mixed models and magnitude-based decisions were used for analysis. Results: The analysis of sleep schedules showed that U16 players went to bed and woke up later than their older counterparts (small differences). In general, players obtained seven hours of sleep per night, with trivial or unclear differences between age groups. The validity analysis highlighted a large relationship between objective and subjective sleep measures for bedtime (r = 0.56 [0.48 to 0.63]), and get up time (r = 0.70 [0.63 to 0.75]). A large standardised typical error (1.50 [1.23 to 1.88]) was observed for total sleep time. Conclusion: This study highlights that differences exist in sleep schedules between rugby union players in different age categories that should be considered when planning training. Additionally, self-reported measures overestimated sleep parameters. Coaches should consider these results to optimise sleep habits of their players and should be careful with self-reported sleep measures.

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